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bitrl & cuberl Documentation
Simulation engine for reinforcement learning agents
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Functions | |
| dict | load_policy (Path filename) |
Variables | |
| policy_path = Path('/home/alex/qi3/cuberl/build/examples/rl/rl_example_10/policy.csv') | |
| dict | policy = load_policy(policy_path) |
| int | max_episode_steps = 200 |
| str | version = 'v0' |
| str | env_tag = f"CliffWalking-{version}" |
| env | |
| state = observation | |
| _ | |
| bool | done = False |
| int | total_reward = 0 |
| dict | action = policy[state] |
| observation | |
| reward | |
| truncated | |
| info | |
| bool | is_slippery = False |
| dict play.load_policy | ( | Path | filename | ) |
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protected |
| bool play.done = False |
| play.env |
| str play.env_tag = f"CliffWalking-{version}" |
| play.info |
| bool play.is_slippery = False |
| int play.max_episode_steps = 200 |
| play.observation |
| dict play.policy = load_policy(policy_path) |
| play.policy_path = Path('/home/alex/qi3/cuberl/build/examples/rl/rl_example_10/policy.csv') |
| play.reward |
| play.state = observation |
| int play.total_reward = 0 |
| play.truncated |
| str play.version = 'v0' |